One of the long-term goals of our laboratory is to develop and combine different analytical and computer simulation techniques into a unified computational modeling scheme to investigate B cell responses. We are developing this proposed in silico model specifically to investigate the mechanism of B cell immune recognition, signaling, and activation. Computational modeling will be carried out synergistically with biological experiments in an iterative manner. The stochastic character of our model enables us to capture any cell-to-cell stochastic fluctuations present in the system. We rigorously map the probabilistic parameters of our model to the reaction and diffusion rate constants that are measurable in biological experiments. This proposed work is going to deliver the following: (i) computational and theoretical approaches to investigate B cell receptor clustering in the form of immune synapses and caps for the cases of membrane-bound and soluble antigens respectively, (ii) a computer model of receptor diffusion in a heterogeneous B cell membrane in parallel with single molecules tracking studies, (iii) a computational model of B cell signaling that can be used by B cell immunologists to study B cell antigen recognition, signaling, and activation under a plethora of different physiological conditions by running controlled in silico experiments. Our approach will also be able to address the question of how B cells generate a graded response by recognizing antigens of widely varying affinities. PUBLIC HEALTH RELEVANCE: The B cell immune response is a complex process and a precise mechanism of B cell signaling and activation is not easy to investigate from in vivo and in vitro experiments alone. In the proposed research, we plan to develop computer models of B cell antigen recognition, signaling, and activation. Our computer model will help elucidate the fundamental regulation mechanisms underlying the B cell immune response and could potentially be applied to investigate B cell mediated diseases.